{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow Version: 2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "print('Tensorflow Version: {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataset/credit-a.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>560.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "      <td>824.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0      1      2   3   4   5   6     7   8   9   10  11  12   13     14  15\n",
       "0   0  30.83  0.000   0   0   9   0  1.25   0   0   1   1   0  202    0.0  -1\n",
       "1   1  58.67  4.460   0   0   8   1  3.04   0   0   6   1   0   43  560.0  -1\n",
       "2   1  24.50  0.500   0   0   8   1  1.50   0   1   0   1   0  280  824.0  -1\n",
       "3   0  27.83  1.540   0   0   9   0  3.75   0   0   5   0   0  100    3.0  -1\n",
       "4   0  20.17  5.625   0   0   9   0  1.71   0   1   0   1   2  120    0.0  -1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    357\n",
       "-1    296\n",
       "Name: 15, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:, -1].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:, :-1]\n",
    "y = data.iloc[:, -1].replace(-1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(4, input_shape=(15,), activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(4, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 4)                 64        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 89\n",
      "Trainable params: 89\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['acc']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "653/653 [==============================] - 0s 222us/sample - loss: 283.7279 - acc: 0.5467\n",
      "Epoch 2/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 257.2484 - acc: 0.5467\n",
      "Epoch 3/100\n",
      "653/653 [==============================] - 0s 33us/sample - loss: 233.0358 - acc: 0.5467\n",
      "Epoch 4/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 211.3778 - acc: 0.5467\n",
      "Epoch 5/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 190.8289 - acc: 0.5467\n",
      "Epoch 6/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 172.2546 - acc: 0.5498\n",
      "Epoch 7/100\n",
      "653/653 [==============================] - 0s 35us/sample - loss: 157.3910 - acc: 0.5406\n",
      "Epoch 8/100\n",
      "653/653 [==============================] - 0s 24us/sample - loss: 141.7961 - acc: 0.5314\n",
      "Epoch 9/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 128.7648 - acc: 0.5268\n",
      "Epoch 10/100\n",
      "653/653 [==============================] - 0s 34us/sample - loss: 117.2219 - acc: 0.5237\n",
      "Epoch 11/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 106.3069 - acc: 0.5237\n",
      "Epoch 12/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 97.1894 - acc: 0.5314\n",
      "Epoch 13/100\n",
      "653/653 [==============================] - 0s 32us/sample - loss: 88.8153 - acc: 0.5329\n",
      "Epoch 14/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 80.5441 - acc: 0.5360\n",
      "Epoch 15/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 73.6151 - acc: 0.5345\n",
      "Epoch 16/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 67.1415 - acc: 0.5115\n",
      "Epoch 17/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 61.2143 - acc: 0.5023\n",
      "Epoch 18/100\n",
      "653/653 [==============================] - 0s 40us/sample - loss: 55.8352 - acc: 0.4931\n",
      "Epoch 19/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 50.6759 - acc: 0.4977\n",
      "Epoch 20/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 46.6285 - acc: 0.5069\n",
      "Epoch 21/100\n",
      "653/653 [==============================] - 0s 34us/sample - loss: 42.5317 - acc: 0.4992\n",
      "Epoch 22/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 38.9789 - acc: 0.5008\n",
      "Epoch 23/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 35.2578 - acc: 0.5023\n",
      "Epoch 24/100\n",
      "653/653 [==============================] - ETA: 0s - loss: 18.0825 - acc: 0.50 - 0s 31us/sample - loss: 32.4101 - acc: 0.5054\n",
      "Epoch 25/100\n",
      "653/653 [==============================] - 0s 35us/sample - loss: 29.4445 - acc: 0.5008\n",
      "Epoch 26/100\n",
      "653/653 [==============================] - 0s 23us/sample - loss: 26.5672 - acc: 0.5130\n",
      "Epoch 27/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 24.1787 - acc: 0.5222\n",
      "Epoch 28/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 22.0136 - acc: 0.5283\n",
      "Epoch 29/100\n",
      "653/653 [==============================] - 0s 34us/sample - loss: 19.8599 - acc: 0.5360\n",
      "Epoch 30/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 18.0299 - acc: 0.5436\n",
      "Epoch 31/100\n",
      "653/653 [==============================] - 0s 37us/sample - loss: 16.2235 - acc: 0.5467\n",
      "Epoch 32/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 14.6557 - acc: 0.5498\n",
      "Epoch 33/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 13.1074 - acc: 0.5498\n",
      "Epoch 34/100\n",
      "653/653 [==============================] - 0s 34us/sample - loss: 11.8988 - acc: 0.5482\n",
      "Epoch 35/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 10.4704 - acc: 0.5482\n",
      "Epoch 36/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 9.2859 - acc: 0.5590\n",
      "Epoch 37/100\n",
      "653/653 [==============================] - 0s 35us/sample - loss: 8.2404 - acc: 0.5636\n",
      "Epoch 38/100\n",
      "653/653 [==============================] - 0s 32us/sample - loss: 7.0988 - acc: 0.5651\n",
      "Epoch 39/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 6.1580 - acc: 0.5666\n",
      "Epoch 40/100\n",
      "653/653 [==============================] - 0s 34us/sample - loss: 5.2377 - acc: 0.5773\n",
      "Epoch 41/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 4.4237 - acc: 0.5835\n",
      "Epoch 42/100\n",
      "653/653 [==============================] - 0s 37us/sample - loss: 3.5901 - acc: 0.5896\n",
      "Epoch 43/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 2.9218 - acc: 0.6126\n",
      "Epoch 44/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 2.2633 - acc: 0.6172\n",
      "Epoch 45/100\n",
      "653/653 [==============================] - 0s 38us/sample - loss: 1.6878 - acc: 0.6233\n",
      "Epoch 46/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 1.2063 - acc: 0.6340\n",
      "Epoch 47/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 0.8285 - acc: 0.6585\n",
      "Epoch 48/100\n",
      "653/653 [==============================] - 0s 34us/sample - loss: 0.6547 - acc: 0.6692\n",
      "Epoch 49/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6543 - acc: 0.6692\n",
      "Epoch 50/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6538 - acc: 0.6692\n",
      "Epoch 51/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6533 - acc: 0.6692\n",
      "Epoch 52/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 0.6526 - acc: 0.6692\n",
      "Epoch 53/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 0.6521 - acc: 0.6708\n",
      "Epoch 54/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 0.6517 - acc: 0.6692\n",
      "Epoch 55/100\n",
      "653/653 [==============================] - 0s 24us/sample - loss: 0.6514 - acc: 0.6662\n",
      "Epoch 56/100\n",
      "653/653 [==============================] - 0s 32us/sample - loss: 0.6507 - acc: 0.6677\n",
      "Epoch 57/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6501 - acc: 0.6677\n",
      "Epoch 58/100\n",
      "653/653 [==============================] - 0s 24us/sample - loss: 0.6500 - acc: 0.6692\n",
      "Epoch 59/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 0.6494 - acc: 0.6677\n",
      "Epoch 60/100\n",
      "653/653 [==============================] - 0s 34us/sample - loss: 0.6486 - acc: 0.6692\n",
      "Epoch 61/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6483 - acc: 0.6677\n",
      "Epoch 62/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 0.6480 - acc: 0.6662\n",
      "Epoch 63/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6474 - acc: 0.6677\n",
      "Epoch 64/100\n",
      "653/653 [==============================] - 0s 32us/sample - loss: 0.6475 - acc: 0.6677\n",
      "Epoch 65/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6461 - acc: 0.6631\n",
      "Epoch 66/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 0.6460 - acc: 0.6692\n",
      "Epoch 67/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 0.6455 - acc: 0.6692\n",
      "Epoch 68/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 0.6458 - acc: 0.6708\n",
      "Epoch 69/100\n",
      "653/653 [==============================] - 0s 24us/sample - loss: 0.6444 - acc: 0.6692\n",
      "Epoch 70/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6440 - acc: 0.6692\n",
      "Epoch 71/100\n",
      "653/653 [==============================] - 0s 32us/sample - loss: 0.6435 - acc: 0.6692\n",
      "Epoch 72/100\n",
      "653/653 [==============================] - 0s 30us/sample - loss: 0.6432 - acc: 0.6708\n",
      "Epoch 73/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6430 - acc: 0.6692\n",
      "Epoch 74/100\n",
      "653/653 [==============================] - 0s 34us/sample - loss: 0.6423 - acc: 0.6708\n",
      "Epoch 75/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6419 - acc: 0.6692\n",
      "Epoch 76/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6413 - acc: 0.6738\n",
      "Epoch 77/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 0.6412 - acc: 0.6753\n",
      "Epoch 78/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6403 - acc: 0.6753\n",
      "Epoch 79/100\n",
      "653/653 [==============================] - 0s 32us/sample - loss: 0.6399 - acc: 0.6753\n",
      "Epoch 80/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6396 - acc: 0.6753\n",
      "Epoch 81/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6392 - acc: 0.6769\n",
      "Epoch 82/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6388 - acc: 0.6769\n",
      "Epoch 83/100\n",
      "653/653 [==============================] - 0s 32us/sample - loss: 0.6382 - acc: 0.6784\n",
      "Epoch 84/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6379 - acc: 0.6799\n",
      "Epoch 85/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6373 - acc: 0.6799\n",
      "Epoch 86/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 0.6371 - acc: 0.6815\n",
      "Epoch 87/100\n",
      "653/653 [==============================] - 0s 27us/sample - loss: 0.6367 - acc: 0.6815\n",
      "Epoch 88/100\n",
      "653/653 [==============================] - 0s 24us/sample - loss: 0.6362 - acc: 0.6815\n",
      "Epoch 89/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 0.6359 - acc: 0.6830\n",
      "Epoch 90/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6358 - acc: 0.6815\n",
      "Epoch 91/100\n",
      "653/653 [==============================] - 0s 32us/sample - loss: 0.6356 - acc: 0.6815\n",
      "Epoch 92/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6356 - acc: 0.6830\n",
      "Epoch 93/100\n",
      "653/653 [==============================] - 0s 29us/sample - loss: 0.6342 - acc: 0.6861\n",
      "Epoch 94/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6338 - acc: 0.6876\n",
      "Epoch 95/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6330 - acc: 0.6861\n",
      "Epoch 96/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 0.6328 - acc: 0.6891\n",
      "Epoch 97/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 0.6320 - acc: 0.6891\n",
      "Epoch 98/100\n",
      "653/653 [==============================] - 0s 24us/sample - loss: 0.6316 - acc: 0.6861\n",
      "Epoch 99/100\n",
      "653/653 [==============================] - 0s 31us/sample - loss: 0.6314 - acc: 0.6876\n",
      "Epoch 100/100\n",
      "653/653 [==============================] - 0s 26us/sample - loss: 0.6313 - acc: 0.6907\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x, y, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x260bca03588>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history.get('loss'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x260bca569e8>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history.get('acc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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